Recomendação de Ações Pedagógicas Utilizando Planejamento Automático e Taxonomia Digital de Bloom
Resumo
Este trabalho apresenta uma abordagem para recomendação de ações pedagógicas em Ambiente Virtual de Aprendizagem (AVA), utilizando planejamento automático. As ações consideradas pelo planejador são estruturadas pela Taxonomia Digital de Bloom. O estado é especificado de acordo com o modelo do estudante. Esse modelo é composto por metadados gerados a partir de AVA e informações coletadas através do questionário ASSIST. Na abordagem existe uma base de casos contendo planos associados a modelos de estudantes com a finalidade de otimizar o processo. Uma prova de conceito mostra a viabilidade da utilização do Planejamento Automático de maneira efetiva no contexto de recomendações de ações pedagógicas genéricas.
Palavras-chave:
Planejamento Automático, Taxonomia Digital de Bloom, Ambiente Virtual de Aprendizagem
Referências
Alonso, C., Gallego, D., and Honey, P. (1999). Los estilos de aprendizaje. procedimientos de diagnóstico y mejora (4ta edición). Bilbao: Ediciones Mensajero.
Brown, S., White, S., Wakeling, L., and Naiker, M. (2015). Approaches and study skills inventory for students (ASSIST) in an introductory course in chemistry. Journal of University Teaching & Learning Practice.
Caputi, V. and Garrido, A. (2015). Student-oriented planning of e-learning contents for Moodle. Journal of Network and Computer Applications, 53:115 – 127.
Churches, A. (2010). Bloom’s digital taxonomy.
Costa, N., Pereira Junior, C., Araújo, R., and Fernandes, M. (2019). Application of AI planning in the context of e-learning. In International Conference on Advanced Learning Technologies (ICALT), page 57.
Dantas, A. C., de Melo, S., Fernandes, M., Lima, L., and do Nascimento, M. Z. (2018). Recomendação de estratégias pedagógicas através de emoções, perfis de personalidade e inteligências múltiplas utilizando raciocínio baseado em casos. In Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação-SBIE), volume 29, page 1213.
de Paiva, R. A. and Padilha, M. A. S. (2012). A WebQuest e a taxonomia digital de Bloom como uma nova coreografia didática para a educação online. Revista Brasileira de Ensino de Ciência e Tecnologia, 5(1).
Entwistle, N. and McCune, V. (2004). The conceptual bases of study strategy inventories. Educational Psychology Review, 16(4):325–345.
Felder, R. M., Silverman, L. K., et al. (1988). Learning and teaching styles in engineering education. Engineering education, 78(7):674–681.
Garrido, A. and Morales, L. (2014). E-learning and intelligent planning: Improving content personalization. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, 9(1):1–7.
Garrido, A., Morales, L., and Serina, I. (2016). On the use of case-based planning for e-learning personalization. Expert Systems with Applications, 60:1–15.
Garrido, A. and Onaindia, E. (2013). Assembling learning objects for personalized learning: An AI planning perspective. IEEE Intelligent Systems, 28(2):64–73.
Haendchen Filho, A., Tomazoni, E. K., Paza, R., Perego, R., and Raabe, A. (2018). Bloom’s taxonomy-based approach for assisting formulation and automatic short answer grading. In Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação-SBIE), volume 29, page 238.
Krathwohl, D. R. (2002). A revision of Bloom’s taxonomy: An overview. Theory Into Practice, 41(4):212–218.
Leu, D. J., Kinzer, C. K., Coiro, J., Castek, J., and Henry, L. A. (2017). New literacies: A dual-level theory of the changing nature of literacy, instruction, and assessment. Journal of Education, 197(2):1–18.
Limongelli, C. and Sciarrone, F. (2014). Fuzzy student modeling for personalization of e-learning courses. In Zaphiris, P. and Ioannou, A., editors, Learning and Collaboration Technologies. Designing and Developing Novel Learning Experiences, pages 292–301, Cham. Springer International Publishing.
Marinov, M. and Valova, I. (2016). Application of planning techniques in knowledge-managed tutoring systems. In 2016 15th International Conference on Information Technology Based Higher Education and Training (ITHET), pages 1–5.
Nau, D., Ghallab, M., and Traverso, P. (2004). Automated Planning: Theory & Practice. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.
Ohler, J. B. (2013). Digital storytelling in the classroom: New media pathways to literacy, learning, and creativity. Corwin Press.
Pireva, K. and Kefalas, P. (2018). A recommender system based on hierarchical clustering for cloud e-learning. Intelligent Distributed Computing XI, 53:235 – 245.
Sanchez Nigenda, R., Maya Padrón, C., Martínez-Salazar, I., and Torres-Guerrero, F. (2017). Design and evaluation of planning and mathematical models for generating learning paths. Computational Intelligence, 34(3):821–838.
Torres, I.-D. and Guzmán-Luna, J. (2015). Reactive planning to compose learning routes in uncertain environments. In New Trends in Networking, Computing, E-learning, Systems Sciences, and Engineering, pages 101–108. Springer.
Xu, D., Huang, W. W., Wang, H., and Heales, J. (2014). Enhancing e-learning effectiveness using an intelligent agent-supported personalized virtual learning environment: An empirical investigation. Information & Management, 51(4):430 – 440.
Brown, S., White, S., Wakeling, L., and Naiker, M. (2015). Approaches and study skills inventory for students (ASSIST) in an introductory course in chemistry. Journal of University Teaching & Learning Practice.
Caputi, V. and Garrido, A. (2015). Student-oriented planning of e-learning contents for Moodle. Journal of Network and Computer Applications, 53:115 – 127.
Churches, A. (2010). Bloom’s digital taxonomy.
Costa, N., Pereira Junior, C., Araújo, R., and Fernandes, M. (2019). Application of AI planning in the context of e-learning. In International Conference on Advanced Learning Technologies (ICALT), page 57.
Dantas, A. C., de Melo, S., Fernandes, M., Lima, L., and do Nascimento, M. Z. (2018). Recomendação de estratégias pedagógicas através de emoções, perfis de personalidade e inteligências múltiplas utilizando raciocínio baseado em casos. In Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação-SBIE), volume 29, page 1213.
de Paiva, R. A. and Padilha, M. A. S. (2012). A WebQuest e a taxonomia digital de Bloom como uma nova coreografia didática para a educação online. Revista Brasileira de Ensino de Ciência e Tecnologia, 5(1).
Entwistle, N. and McCune, V. (2004). The conceptual bases of study strategy inventories. Educational Psychology Review, 16(4):325–345.
Felder, R. M., Silverman, L. K., et al. (1988). Learning and teaching styles in engineering education. Engineering education, 78(7):674–681.
Garrido, A. and Morales, L. (2014). E-learning and intelligent planning: Improving content personalization. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, 9(1):1–7.
Garrido, A., Morales, L., and Serina, I. (2016). On the use of case-based planning for e-learning personalization. Expert Systems with Applications, 60:1–15.
Garrido, A. and Onaindia, E. (2013). Assembling learning objects for personalized learning: An AI planning perspective. IEEE Intelligent Systems, 28(2):64–73.
Haendchen Filho, A., Tomazoni, E. K., Paza, R., Perego, R., and Raabe, A. (2018). Bloom’s taxonomy-based approach for assisting formulation and automatic short answer grading. In Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação-SBIE), volume 29, page 238.
Krathwohl, D. R. (2002). A revision of Bloom’s taxonomy: An overview. Theory Into Practice, 41(4):212–218.
Leu, D. J., Kinzer, C. K., Coiro, J., Castek, J., and Henry, L. A. (2017). New literacies: A dual-level theory of the changing nature of literacy, instruction, and assessment. Journal of Education, 197(2):1–18.
Limongelli, C. and Sciarrone, F. (2014). Fuzzy student modeling for personalization of e-learning courses. In Zaphiris, P. and Ioannou, A., editors, Learning and Collaboration Technologies. Designing and Developing Novel Learning Experiences, pages 292–301, Cham. Springer International Publishing.
Marinov, M. and Valova, I. (2016). Application of planning techniques in knowledge-managed tutoring systems. In 2016 15th International Conference on Information Technology Based Higher Education and Training (ITHET), pages 1–5.
Nau, D., Ghallab, M., and Traverso, P. (2004). Automated Planning: Theory & Practice. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.
Ohler, J. B. (2013). Digital storytelling in the classroom: New media pathways to literacy, learning, and creativity. Corwin Press.
Pireva, K. and Kefalas, P. (2018). A recommender system based on hierarchical clustering for cloud e-learning. Intelligent Distributed Computing XI, 53:235 – 245.
Sanchez Nigenda, R., Maya Padrón, C., Martínez-Salazar, I., and Torres-Guerrero, F. (2017). Design and evaluation of planning and mathematical models for generating learning paths. Computational Intelligence, 34(3):821–838.
Torres, I.-D. and Guzmán-Luna, J. (2015). Reactive planning to compose learning routes in uncertain environments. In New Trends in Networking, Computing, E-learning, Systems Sciences, and Engineering, pages 101–108. Springer.
Xu, D., Huang, W. W., Wang, H., and Heales, J. (2014). Enhancing e-learning effectiveness using an intelligent agent-supported personalized virtual learning environment: An empirical investigation. Information & Management, 51(4):430 – 440.
Publicado
11/11/2019
Como Citar
COSTA, Newarney T.; PEREIRA JUNIOR, Cleon X.; FERNANDES, Márcia A..
Recomendação de Ações Pedagógicas Utilizando Planejamento Automático e Taxonomia Digital de Bloom. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 30. , 2019, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 1531-1540.
DOI: https://doi.org/10.5753/cbie.sbie.2019.1531.
